Metadata-Version: 1.1
Name: AutomaticAI
Version: 0.3
Summary: A Hybrid Approach for Automatic Artificial Intelligence Algorithm Selection and Hyperparameter Tuning
Home-page: https://github.com/CzakoZoltan08/AutomaticAI
Author: Czako Zoltan
Author-email: czakozoltan08@gmail.com
License: MIT
Download-URL: https://github.com/CzakoZoltan08/AutomaticAI/archive/v_01.tar.gz
Description: 
        AutomaticAI - A Hybrid Approach for Automatic Artificial Intelligence Algorithm Selection and Hyperparameter Tuning
        ===================================================================================================================
        
        Introduction
        ----------------
        
        This is method used for solving the problem of AI algorithm selection and hyperparameter tuning, without human intervention, in a fully automated way. The method is a hybrid approach, a combination between Particle Swarm Optimization and the Simulated Annealing.
        
        
        Example Usage
        ----------------
        
        from sklearn import datasets
        
        from sklearn.model_selection import train_test_split
        
        from sklearn.metrics import accuracy_score
        
        from AutoAIAlgorithm.ParticleSwarmOptimization import PSO
        
        
        
        def main():
            # load the MNIST digits dataset
        
            mnist = datasets.load_digits()
            
            X = mnist.data
        
            y = mnist.target
           
            # Splitting the data into training set, test set and validation set
        
            x_train, x_test, y_train, y_test = train_test_split(X, y)
        
            num_particles=5
        
            num_iterations=30
            
            pso = PSO(particle_count=num_particles,
                      distance_between_initial_particles=0.7,
                      evaluation_metric=accuracy_score)
            
            best_metric, best_model = pso.fit(X_train=x_train,
                                              X_test=x_test,
                                              Y_train=y_train,
                                              Y_test=y_test,
                                              maxiter=num_iterations,
                                              verbose=True,
                                              max_distance=0.05)
                    
            print(best_metric)
            print(best_model)
        
        
        if __name__ == "__main__":
            main()
Keywords: AI,Arificial Intelligence,Particle Swarm Optimization,Simulated Annealing,Automatic Algorithm Selection,Automatic parameter optimization
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
